Experimental Design: from Agriculture to Industry and

Marketing Research

Utami Dyah Syafitri

Email: utamids@apps.ipb.ac.id

Seminar Online : 27 Februari 2021

Program Studi Statistika dan Sains Data

Departemen Statistika FMIPA

IPB University

Departemen Statistika FMIPA IPB 1

Outline

Definition of Experimental Design

Experimental Design in Industry – Factorial Design

Experimental Design in Market Research – Conjoint analysis

Further research

Departemen Statistika FMIPA IPB 2

Methods of collection data

Departemen Statistika FMIPA IPB

survei percobaanobservasi

database

(administrasi, transaksi,

tangkapan aktivitas)

web scraping

sensus

3

Observasi vs Percobaan

Pengamatan terhadap perubahan warna

Apakah jika diberikan warna yang berbeda, maka perubahan warnanya akan

sesuai dengan warna yang diberikan?

Sumber foto: https://i.ytimg.com/vi/KV4YuzuXpjQ/maxresdefault.jpgSumber foto: https://carecorner.weebly.com/uploads/7/0/4/2/7042382/6001608_orig.jpg Departemen Statistika FMIPA IPB 4

Experiment vs Experimental design

Sumber : https://www.scribbr.com/methodology/experimental-design/Departemen Statistika FMIPA IPB 5

Experiment

• An experiment is a type of research method in which you manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental Design

• creating a set of procedures to test a hypothesis.

Source : http://www.stat.cmu.edu/~hseltman/309/Book/Book.pdf

Departemen Statistika FMIPA IPB 6

APA ITU PERANCANGAN PERCOBAAN?

• Merupakan suatu metode yang sistematik yang didalamnya terdapat uji atau sederetan uji dimana suatuproses atau sistem mengakibatkan terjadinya perubahanyang cukup berarti dari variabel input, yang dapatdiamati melalui respon yang muncul.

• Perencanaan (planning) suatu percobaan untukmemperoleh informasi yang relevan dengan tujuan daripenelitian

Departemen Statistika FMIPA IPB 7

ProsesOutput

Y1, Y2, …, Yk

Inputs

. . .

Controllable factors

. . .

Uncontrollable factors

X1X2 Xp

Z1 Z2 Zp

Sumber: Montgomery, 2013.Design and analysis of experiments 8th edition. Wiley

Departemen Statistika FMIPA IPB 8

Cause and Effects Diagram

Objective of experiments

Blocking factors

Held constant factors

Controllable factors

Uncontrollable factors

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Experimental Design in Industry

Departemen Statistika FMIPA IPB 10

Syzygium polyanthum (Wight) WalpBay Leaves

as medicinal plant

Another names: Salam|Maselangan|Ubar Serai|Gowok|Kastolam

Pharmacological Effects of

Bay Leaves

Antioxidant Antibacterial AntidiarrhealAlzheimer

treatment

ACTIVITIESCOMPONENTS

Flavonoids

Phenolics

EXTRACTION TECHNIQUE

Microwave-Assisted Extraction (MAE)

♥ Higher yield

♥ Much shorter time

♥ High efficiency in solvent consuming

Factors Levels(Variables, Inputs) (Settings)

Study case: Bay leaves extraction

Time

Power

Solvent to sample ratio

Treatment(s)

30s 45s 60s

30%

30 mL/g

(1) 30s

(4) 45s

(8) 30s

(2) 45s

(6) 60s

(9) 60s

(3) 30s

(7) 45s

(10) 60s

Replications : 3x

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Analysing the data

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1

2

3

One factor at time

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Power : 30%Solvent to sample ratio: 30 ml/gram

Factors Levels(Variables, Inputs) (Settings)

Study case: Bay leave extraction

Time

Power

Solvent to sample ratio

Treatment(s)

45s

10% 30% 50%

30 mL/g

(1) 10%

(4) 30%

(7) 50%

(2) 50%

(5) 10%

(8) 10%

(3) 30%

(6) 50%

(9) 30%

Replications : 3x

Departemen Statistika FMIPA IPB 17

Departemen Statistika FMIPA IPB 18

Departemen Statistika FMIPA IPB 19

• Tidak bisamembandingkan semuakombinasi perlakuan

• Pengacakan lengkaptidak bisa dilakukankarena percobaandilakukan secarabertahap -> pengacakanmerupakan salah satuprinsip dalamperancangan percobaan

Drawbacks

Factorial design

20

•Dalam berbagai bidang penerapan perancanganpercobaan diketahui bahwa respon dari individumerupakan akibat dari berbagai faktor secarasimultan•Percobaan satu faktor akan menjadi sangat tidak

efektif mengingat respon yang muncul akan berbedajika kondisi faktor-faktor lain berubah•Percobaan faktorial dicirikan oleh perlakuan yang

merupakan komposisi dari semua kemungkinankombinasi dari level-level dua faktor atau lebih

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Factors Levels Responses

(Variables, Inputs) (Settings) (Outcomes, characteristics)

Flavonoid

Phenolics

Study case: Bay leaves extraction

Time

Power

Solvent to sample ratio

Treatment(s)

30s 45s 60s

10% 30% 50%

20 mL/g 30 mL/g 40 mL/g

30s

10%

40 mL/g

Departemen Statistika FMIPA IPB 21

Sumber : Anggraini, D.N. 2016. Pengoptimuman kondisi ekstraksi berbantuan mikrogelombang untuk fenol dan flavonoid total daun salam menggunakan metode permukaan. Skripsi. Kimia.

33 Factorial Design - All treatments

Departemen Statistika FMIPA IPB 22

20 ml/gram

10%

30%

50%

30 s

45 s

60 s

30 s

45 s

60 s

30 s

45 s

60 s

30 ml/gram

10%

30%

50%

30 s

45 s

60 s

30 s

45 s

60 s

30 s

45 s

60 s

40 ml/gram

10%

30%

50%

30 s

45 s

60 s

30 s

45 s

60 s

30 s

45 s

60 s

Experimental Region – 33 Factorial Design

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C

A

B

20 mg/liter 40 mg/liter

10%

50%

30%

60%

Prinsip Perancangan Percobaan

Randomization (Pengacakan)

Replication (Ulangan)

Local Control -- Blocking

Factorial design in Complete Randomized Block Design (CRBD)

25

Block I Block II

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RandomizationPengamatan

ke- ...Kondisi ekstraksi

A B C1 1 1 22 3 2 13 3 2 34 1 2 15 2 2 26 1 3 27 3 1 18 2 3 29 3 2 2

10 1 1 111 3 1 212 2 1 113 2 2 114 1 3 115 3 1 316 2 1 217 1 3 318 3 3 219 2 1 320 1 1 321 2 3 322 3 3 123 2 3 124 1 2 225 3 3 326 1 2 327 2 2 3

Pengamatan ke- ...

Kondisi ekstraksiA B C

28 3 2 329 3 3 330 2 3 331 2 2 232 3 3 133 1 2 134 1 3 335 1 1 136 2 3 237 2 2 138 1 3 239 3 2 140 1 1 341 3 1 142 1 2 243 1 2 344 2 1 145 3 2 246 1 1 247 3 3 248 2 1 349 1 3 150 3 1 351 2 3 152 2 1 253 3 1 254 2 2 3Departemen Statistika FMIPA IPB 26

Departemen Statistika FMIPA IPB 27

Response Surface Model

Total Flavonoids

Power 10% Power 30% Power 50%Departemen Statistika FMIPA IPB 28

Sumber : Anggraini, D.N. 2016. Pengoptimuman kondisi ekstraksi berbantuan mikrogelombang untuk fenol dan flavonoid total daun salam menggunakan metode permukaan. Skripsi. Kimia.

Experimental Design in Marketing Research

Departemen Statistika FMIPA IPB 29

Conjoin Analysis

Dalam marketing riset merupakan suatu tehnikpeubah ganda yang dikembangkan secara khusus

untuk mengetahui preferensi dari berbagaiobjek (produk, layanan, atau ide)

Preferences of statistics teaching methods

Preferences of bike

Three important steps in Conjoint Analysis

Design the combination of attributes (experimental design)

Sampling

Analysis

Tahapan (1)

Tahap I

• Definisipermasalahan

Tahap II

• PemilihanMetode Konjoin

• Merancangstimuli

• Merancangbagaimanastimuli diukur

• Merancangkuesioner

Tahap III

• Keterpenuhanasumsi darianalisis konjoin

Tahapan (2)

Tahap IV

• Pendugaanmodel konjoin

• Evaluasikebaikanmodel

Tahap V

• Interpretasihasil baikdalam level umum maupundalam level individu

• Kepentinganrelatif dariatribut

Tahap VI

• Validasi hasil: internal daneksternalvalidity

Tahap VII

• Aplikasikanhasil konjoinanalisis untuksegmentasipelanggan, analisis profit, choice simulator

Desain-Empat pertanyaan

• Atribut mana yang paling penting dalam menilai preferensi dariresponden – pemilihan atribut

• Bagaimana responden tahu mengenai makna dari masing-masingfaktor – pemilihan level

• Apa yang dievaluasi oleh responden – kombinasi dari atribut profil

• Berapa banyak profil yang dievaluasi -- rancangan

Attributes of teaching methods

• The number of students

• Delivering methods

• Teaching equipments

• Material resources

• Giving rewards

• Giving motivation

• Exercise methods

• Assesment methods

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Levels of each attribute

• The number of students : small, medium, large

• Delivering methods : one way, two way

• Teaching equipments : board, projector, board + projector

• Material resources : handouts, slides, textbooks

• Giving rewards : always, sometimes, no

• Giving motivation : always, sometimes, no

• Exercise methods : individual, groups

• Assesment methods : standard, distribution

Departemen Statistika FMIPA IPB 37

Profiles

Departemen Statistika FMIPA IPB 38

BoardHandouts

Rewards : alwaysMotivation : alwaysExercise : individual

Assesment : standard

ProjectorHandouts

Rewards : alwaysMotivation : alwaysExercise : individual

Assesment : standard

ProjectorHandouts

Rewards : sometimesMotivation : alwaysExercise : individual

Assesment : standard

Experimental design

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• The number of students : small, medium, large

• Delivering methods : one way, two way

• Teaching equipments : board, projector, board + projector

• Material resources : handouts, slides, textbooks

• Giving rewards : always, sometimes, no

• Giving motivation : always, sometimes, no

• Exercise methods : individual, groups

• Assesment methods : standard, distribution

All combination:

35x23

=7776

Impossible to run!

Design

•Karena biasanya jumlah level dan faktor banyak maka tidak digunakan rancangan faktorial

•Rancangan yang biasa digunakan adalah fraksional faktorial atau bridging design atau orthogonal array atau rancangan yang optimal berdasarkan kriteria tertentu

Another isu : Response measurementTraditional conjoint

Rank

1 10 1 10 1 10Rating

Response measurementChoice Based Conjoint

Our research : Five atributes/factors*

Delivering methods (A)

One way (1)

Two way (-1)

Teaching equipments (B)

white board (1)

white board + projector (-1)

Material sources (C)

Slides/handout (1)

Textbooks (-1)

Giving rewards (D)

Exist (1)

Not exist (-1)

Giving motivation (E)

Exist (1)

Not exist (-1)

Syafitri U，Afandi FM, Palupi SP (2016). Choice Based Conjoint for Preferences of Statistics Teaching Methods. Proceeding of the 7th Annual Basic Science International Conference-2017. FMIPA Universitas Brawijaya

25 Full Factorial Design NO A B C D E

1 1 1 1 1 1

2 1 1 1 1 -1

3 1 1 1 -1 1

4 1 1 1 -1 -1

5 1 1 -1 1 1

6 1 1 -1 1 -1

7 1 1 -1 -1 1

8 1 1 -1 -1 -1

9 1 -1 1 1 1

.

.

.

32 -1 -1 -1 -1 -1

A profile

One way, using only white board

Sources of material : Slides/handout

Giving rewards for students

Giving motivation in the class

Profile no 1

Profile no 9

One way, using white board + projector

Sources of material : Slides/handout

Giving rewards for students

Giving motivation in the class

25 Full Factorial Design NO A B C D E

1 1 1 1 1 1

2 1 1 1 1 -1

3 1 1 1 -1 1

4 1 1 1 -1 -1

5 1 1 -1 1 1

6 1 1 -1 1 -1

7 1 1 -1 -1 1

8 1 1 -1 -1 -1

9 1 -1 1 1 1

.

.

.

32 -1 -1 -1 -1 -1

A profile

Too Much!!!

Fractional Factorial

8 profiles 8 profiles

8 profiles 8 profiles

Task 1 Task 2

Task 3 Task 4

Task 1No A B C D E

1 1 1 1 1 1

2 1 1 -1 1 -1

3 1 -1 1 -1 -1

4 1 -1 -1 -1 1

5 -1 1 1 -1 -1

6 -1 1 -1 -1 1

7 -1 -1 1 1 1

8 -1 -1 -1 1 -1

Generators : D = AB, E = ABC

+1

Task 2No A B C D E

1 1 1 1 -1 1

2 1 1 -1 -1 -1

3 1 -1 1 1 -1

4 1 -1 -1 1 1

5 -1 1 1 1 -1

6 -1 1 -1 1 1

7 -1 -1 1 -1 1

8 -1 -1 -1 -1 -1

Generators : D = -AB, E = ABC

-1

Task 3No A B C D E

1 1 1 1 -1 -1

2 1 1 -1 -1 1

3 1 -1 1 1 1

4 1 -1 -1 1 -1

5 -1 1 1 1 1

6 -1 1 -1 1 -1

7 -1 -1 1 -1 -1

8 -1 -1 -1 -1 1

Generators : D = -AB, E = -ABC

+1

Task 4

No A B C D E

1 1 1 1 1 -1

2 1 1 -1 1 1

3 1 -1 1 -1 1

4 1 -1 -1 -1 -1

5 -1 1 1 -1 1

6 -1 1 -1 -1 -1

7 -1 -1 1 1 -1

8 -1 -1 -1 1 1

Generators : D = AB, E = -ABC

-1

Sampling Methodology

• Population : Students of Department of Statistics, Faculty of Mathematics and Natural Sciencies , Bogor Agricultural University

• Stratified Random sampling : 2nd batch, 3rd batch, and 4th batch

• Total respondents : 150

• Distribution respondents of each batch

53 50 47

2nd batch 3rd batch 4th batch

Distribution of respondents/task

12 121112 1412

14 1212 15 1212

Task 1 Task 2

Task 3 Task 4

35 38

38 39

Analysis

• The X matrix is a full factorial from 4 tasks

• The response is 1 (yes) and 0 (no)

• Logistic binary regression is used to estimate the coefficients of the model which uses for utility values of product

• Total utility function Coefficient of a logistic regression

Utility value of each level of each attribute

Important relative values

Conclusion

• The preferable teaching method for statistics students were : delivering methods in two ways, using white board and projector, no matter material sources, existing rewards and motivations in the class.

• The most important attribute was teaching equipment. The next two important attributes were delivering methods and motivation. Source of materials was not important attribute at all.

• The preferable teaching method for statistics students were : delivering methods in two ways, using white board and projector, existing rewards and motivations in the class.

• The most important attribute was teaching equipment for visualization.

Further research

Departemen Statistika FMIPA IPB 58

More factors and levels

Blocking factors

Model : linear vs non linear

model

Restricted on budget

Type of factors

Restricted on experimental

region

Availability constraint

Other constraints

Orthogonal array

Optimal Design

The secret of change is to focus all of your energy not on fighting the old,

but building the new

Socrates

The people who are crazy enough to think they can change the world are the

ones who do

Think different

Departemen Statistika FMIPA IPB 60

Thank you